import colorsys
import os
import time

import numpy as np
import torch
import torch.nn as nn
from PIL import ImageDraw, ImageFont

from nets.yolo import YoloBody
from utils.utils import (cvtColor, get_anchors, get_classes, preprocess_input,
                         resize_image)
from utils.utils_bbox import DecodeBox

'''
训练自己的数据集必看注释！
'''


class YOLO(object):
    _defaults = {
        # --------------------------------------------------------------------------#
        #   使用自己训练好的模型进行预测一定要修改model_path和classes_path！
        #   model_path指向logs文件夹下的权值文件，classes_path指向model_data下的txt
        #
        #   训练好后logs文件夹下存在多个权值文件，选择验证集损失较低的即可。
        #   验证集损失较低不代表mAP较高，仅代表该权值在验证集上泛化性能较好。
        #   如果出现shape不匹配，同时要注意训练时的model_path和classes_path参数的修改
        # --------------------------------------------------------------------------#
        "model_path": 'model_data/yolo_weights.pth',
        "classes_path": 'model_data/coco_classes.txt',
        # ---------------------------------------------------------------------#
        #   anchors_path代表先验框对应的txt文件，一般不修改。
        #   anchors_mask用于帮助代码找到对应的先验框，一般不修改。
        # ---------------------------------------------------------------------#
        "anchors_path": 'model_data/yolo_anchors.txt',
        "anchors_mask": [[6, 7, 8], [3, 4, 5], [0, 1, 2]],
        # ---------------------------------------------------------------------#
        #   输入图片的大小，必须为32的倍数。
        # ---------------------------------------------------------------------#
        "input_shape": [416, 416],
        # ---------------------------------------------------------------------#
        #   只有得分大于置信度的预测框会被保留下来
        # ---------------------------------------------------------------------#
        "confidence": 0.5,
        # ---------------------------------------------------------------------#
        #   非极大抑制所用到的nms_iou大小
        # ---------------------------------------------------------------------#
        "nms_iou": 0.3,
        # ---------------------------------------------------------------------#
        #   该变量用于控制是否使用letterbox_image对输入图像进行不失真的resize，
        #   在多次测试后，发现关闭letterbox_image直接resize的效果更好
        # ---------------------------------------------------------------------#
        "letterbox_image": False,
        # -------------------------------#
        #   是否使用Cuda
        #   没有GPU可以设置成False
        # -------------------------------#
        "cuda": False,
    }

    @classmethod
    def get_defaults(cls, n):
        if n in cls._defaults:
            return cls._defaults[n]
        else:
            return "Unrecognized attribute name '" + n + "'"

    # ---------------------------------------------------#
    #   初始化YOLO
    # ---------------------------------------------------#
    def __init__(self, **kwargs):
        self.__dict__.update(self._defaults)
        for name, value in kwargs.items():
            setattr(self, name, value)

        # ---------------------------------------------------#
        #   获得种类和先验框的数量
        # ---------------------------------------------------#
        self.class_names, self.num_classes = get_classes(self.classes_path)
        self.anchors, self.num_anchors = get_anchors(self.anchors_path)
        self.bbox_util = DecodeBox(self.anchors, self.num_classes, (self.input_shape[0], self.input_shape[1]),
                                   self.anchors_mask)

        # ---------------------------------------------------#
        #   画框设置不同的颜色
        # ---------------------------------------------------#
        hsv_tuples = [(x / self.num_classes, 1., 1.) for x in range(self.num_classes)]
        self.colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))
        self.colors = list(map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)), self.colors))
        self.generate()

    # ---------------------------------------------------#
    #   生成模型
    # ---------------------------------------------------#
    def generate(self):
        # ---------------------------------------------------#
        #   建立yolov3模型，载入yolov3模型的权重
        # ---------------------------------------------------#
        self.net = YoloBody(self.anchors_mask, self.num_classes)
        device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
        self.net.load_state_dict(torch.load(self.model_path, map_location=device))
        self.net = self.net.eval()
        print('{} model, anchors, and classes loaded.'.format(self.model_path))

        if self.cuda:
            self.net = nn.DataParallel(self.net)
            self.net = self.net.cuda()

    # ---------------------------------------------------#
    #   检测图片
    # ---------------------------------------------------#
    def detect_image(self, image, crop=False):
        image_shape = np.array(np.shape(image)[0:2])
        # ---------------------------------------------------------#
        #   在这里将图像转换成RGB图像，防止灰度图在预测时报错。
        #   代码仅仅支持RGB图像的预测，所有其它类型的图像都会转化成RGB
        # ---------------------------------------------------------#
        image = cvtColor(image)
        # ---------------------------------------------------------#
        #   给图像增加灰条，实现不失真的resize
        #   也可以直接resize进行识别
        # ---------------------------------------------------------#
        image_data = resize_image(image, (self.input_shape[1], self.input_shape[0]), self.letterbox_image)
        # ---------------------------------------------------------#
        #   添加上batch_size维度
        # ---------------------------------------------------------#
        image_data = np.expand_dims(np.transpose(preprocess_input(np.array(image_data, dtype='float32')), (2, 0, 1)), 0)

        with torch.no_grad():
            images = torch.from_numpy(image_data)
            if self.cuda:
                images = images.cuda()
            # ---------------------------------------------------------#
            #   将图像输入网络当中进行预测！
            # ---------------------------------------------------------#
            outputs = self.net(images)
            outputs = self.bbox_util.decode_box(outputs)
            # ---------------------------------------------------------#
            #   将预测框进行堆叠，然后进行非极大抑制
            # ---------------------------------------------------------#
            results = self.bbox_util.non_max_suppression(torch.cat(outputs, 1), self.num_classes, self.input_shape,
                                                         image_shape, self.letterbox_image, conf_thres=self.confidence,
                                                         nms_thres=self.nms_iou)

            if results[0] is None:
                return image

            top_label = np.array(results[0][:, 6], dtype='int32')
            top_conf = results[0][:, 4] * results[0][:, 5]
            top_boxes = results[0][:, :4]
        # ---------------------------------------------------------#
        #   设置字体与边框厚度
        # ---------------------------------------------------------#
        font = ImageFont.truetype(font='model_data/simhei.ttf',
                                  size=np.floor(3e-2 * image.size[1] + 0.5).astype('int32'))
        thickness = int(max((image.size[0] + image.size[1]) // np.mean(self.input_shape), 1))

        # ---------------------------------------------------------#
        #   是否进行目标的裁剪
        # ---------------------------------------------------------#
        if crop:
            for i, c in list(enumerate(top_label)):
                top, left, bottom, right = top_boxes[i]
                top = max(0, np.floor(top).astype('int32'))
                left = max(0, np.floor(left).astype('int32'))
                bottom = min(image.size[1], np.floor(bottom).astype('int32'))
                right = min(image.size[0], np.floor(right).astype('int32'))

                dir_save_path = "img_crop"
                if not os.path.exists(dir_save_path):
                    os.makedirs(dir_save_path)
                crop_image = image.crop([left, top, right, bottom])
                crop_image.save(os.path.join(dir_save_path, "crop_" + str(i) + ".png"), quality=95, subsampling=0)
                print("save crop_" + str(i) + ".png to " + dir_save_path)
        # ---------------------------------------------------------#
        #   图像绘制
        # ---------------------------------------------------------#
        listed = []  # 存放检测到的数据，格式为 'class', score, x1, y1, x2, y2
        for i, c in list(enumerate(top_label)):
            predicted_class = self.class_names[int(c)]
            box = top_boxes[i]
            score = top_conf[i]

            top, left, bottom, right = box

            top = max(0, np.floor(top).astype('int32'))
            left = max(0, np.floor(left).astype('int32'))
            bottom = min(image.size[1], np.floor(bottom).astype('int32'))
            right = min(image.size[0], np.floor(right).astype('int32'))

            label = '{} {:.2f}'.format(predicted_class, score)
            draw = ImageDraw.Draw(image)
            label_size = draw.textsize(label, font)
            label = label.encode('utf-8')
           #  print(label, top, left, bottom, right)
            listed.append([predicted_class, score, top, left, bottom, right])

            if top - label_size[1] >= 0:
                text_origin = np.array([left, top - label_size[1]])
            else:
                text_origin = np.array([left, top + 1])
            # for i in range(thickness):
            #     draw.rectangle([left + i, top + i, right - i, bottom - i], outline=self.colors[c])
            # draw.rectangle([tuple(text_origin), tuple(text_origin + label_size)], fill=self.colors[c])
            # draw.text(text_origin, str(label, 'UTF-8'), fill=(0, 0, 0), font=font)
            # del draw, listed

        return image, listed

    def get_FPS(self, image, test_interval):
        image_shape = np.array(np.shape(image)[0:2])
        # ---------------------------------------------------------#
        #   在这里将图像转换成RGB图像，防止灰度图在预测时报错。
        #   代码仅仅支持RGB图像的预测，所有其它类型的图像都会转化成RGB
        # ---------------------------------------------------------#
        image = cvtColor(image)
        # ---------------------------------------------------------#
        #   给图像增加灰条，实现不失真的resize
        #   也可以直接resize进行识别
        # ---------------------------------------------------------#
        image_data = resize_image(image, (self.input_shape[1], self.input_shape[0]), self.letterbox_image)
        # ---------------------------------------------------------#
        #   添加上batch_size维度
        # ---------------------------------------------------------#
        image_data = np.expand_dims(np.transpose(preprocess_input(np.array(image_data, dtype='float32')), (2, 0, 1)), 0)

        with torch.no_grad():
            images = torch.from_numpy(image_data)
            if self.cuda:
                images = images.cuda()
            # ---------------------------------------------------------#
            #   将图像输入网络当中进行预测！
            # ---------------------------------------------------------#
            outputs = self.net(images)
            outputs = self.bbox_util.decode_box(outputs)
            # ---------------------------------------------------------#
            #   将预测框进行堆叠，然后进行非极大抑制
            # ---------------------------------------------------------#
            results = self.bbox_util.non_max_suppression(torch.cat(outputs, 1), self.num_classes, self.input_shape,
                                                         image_shape, self.letterbox_image, conf_thres=self.confidence,
                                                         nms_thres=self.nms_iou)

        t1 = time.time()
        for _ in range(test_interval):
            with torch.no_grad():
                # ---------------------------------------------------------#
                #   将图像输入网络当中进行预测！
                # ---------------------------------------------------------#
                outputs = self.net(images)
                outputs = self.bbox_util.decode_box(outputs)
                # ---------------------------------------------------------#
                #   将预测框进行堆叠，然后进行非极大抑制
                # ---------------------------------------------------------#
                results = self.bbox_util.non_max_suppression(torch.cat(outputs, 1), self.num_classes, self.input_shape,
                                                             image_shape, self.letterbox_image,
                                                             conf_thres=self.confidence, nms_thres=self.nms_iou)

        t2 = time.time()
        tact_time = (t2 - t1) / test_interval
        return tact_time

    def get_map_txt(self, image_id, image, class_names, map_out_path):
        f = open(os.path.join(map_out_path, "detection-results/" + image_id + ".txt"), "w")
        image_shape = np.array(np.shape(image)[0:2])
        # ---------------------------------------------------------#
        #   在这里将图像转换成RGB图像，防止灰度图在预测时报错。
        #   代码仅仅支持RGB图像的预测，所有其它类型的图像都会转化成RGB
        # ---------------------------------------------------------#
        image = cvtColor(image)
        # ---------------------------------------------------------#
        #   给图像增加灰条，实现不失真的resize
        #   也可以直接resize进行识别
        # ---------------------------------------------------------#
        image_data = resize_image(image, (self.input_shape[1], self.input_shape[0]), self.letterbox_image)
        # ---------------------------------------------------------#
        #   添加上batch_size维度
        # ---------------------------------------------------------#
        image_data = np.expand_dims(np.transpose(preprocess_input(np.array(image_data, dtype='float32')), (2, 0, 1)), 0)

        with torch.no_grad():
            images = torch.from_numpy(image_data)
            if self.cuda:
                images = images.cuda()
            # ---------------------------------------------------------#
            #   将图像输入网络当中进行预测！
            # ---------------------------------------------------------#
            outputs = self.net(images)
            outputs = self.bbox_util.decode_box(outputs)
            # ---------------------------------------------------------#
            #   将预测框进行堆叠，然后进行非极大抑制
            # ---------------------------------------------------------#
            results = self.bbox_util.non_max_suppression(torch.cat(outputs, 1), self.num_classes, self.input_shape,
                                                         image_shape, self.letterbox_image, conf_thres=self.confidence,
                                                         nms_thres=self.nms_iou)

            if results[0] is None:
                return

            top_label = np.array(results[0][:, 6], dtype='int32')
            top_conf = results[0][:, 4] * results[0][:, 5]
            top_boxes = results[0][:, :4]

        for i, c in list(enumerate(top_label)):
            predicted_class = self.class_names[int(c)]
            box = top_boxes[i]
            score = str(top_conf[i])

            top, left, bottom, right = box
            if predicted_class not in class_names:
                continue

            f.write("%s %s %s %s %s %s\n" % (
            predicted_class, score[:6], str(int(left)), str(int(top)), str(int(right)), str(int(bottom))))

        f.close()
        return
